Systems and Methods for Providing Feedback for Artificial Intelligence-Based Image Capture Devices

ABSTRACT

The present disclosure provides systems and methods that provide feedback to a user of an image capture device that includes an artificial intelligence system that analyzes incoming image frames to, for example, determine whether to automatically capture and store the incoming frames. An example system can also, in the viewfinder portion of a user interface presented on a display, a graphical intelligence feedback indicator in association with a live video stream. The graphical intelligence feedback indicator can graphically indicate, for each of a plurality of image frames as such image frame is presented within the viewfinder portion of the user interface, a respective measure of one or more attributes of the respective scene depicted by the image frame output by the artificial intelligence system.

RELATED APPLICATIONS

This application claims the benefit of US Provisional Patent ApplicationNo. 62/742,810, filed Oct. 8, 2018. US Provisional Patent ApplicationNo. 62/742,810 is hereby incorporated by reference herein in itsentirety.

FIELD

The present disclosure relates generally to systems and methods forcapturing images. More particularly, the present disclosure relates tosystems and methods that provide feedback to a user of an image capturedevice based on an output of an artificial intelligence system thatanalyzes incoming image frames to, for example, measure attributes ofthe incoming frames and/or determine whether to automatically capturethe incoming frames.

BACKGROUND

More and more individuals are using computing devices to capture, store,share, and interact with visual content such as photographs and videos.In particular, for some individuals, handheld computing devices, such asa smartphones or tablets, are the primary devices used to capture visualcontent, such as photographs and videos.

Some example types of photographs that users often capture areself-portrait photographs and group portrait photographs. Inself-portrait photographs, a user typically holds her image capturedevice (e.g., smartphone with camera) such that a front-facing cameracaptures imagery of the user, who is facing the device. The user canalso typically view the current field of view of the camera on afront-facing display screen to determine the attributes and quality ofthe image that is available to be captured. The user can press a shutterbutton capture the image. However, this scenario requires the user tooperate the camera shutter while also attempting to pose for thephotograph. Performing both of these tasks simultaneously can bechallenging and can detract from the enjoyment or success of taking theself-portrait photograph. It can in particular be challenging for theuser to perform these tasks whilst also assessing the attributes of theimage that will be captured when the shutter is operated. This canresult in the captured image having suboptimal lighting effects and/orother undesirable image properties.

In a group portrait photograph, a group of people typically pose for animage together. Historically, group portrait photographs have requiredone member of the party to operate the camera from a position behind thecamera. This results in exclusion of the photographer from thephotograph, which is an unsatisfactory result for both the photographerand the group that wishes for the photographer to join them. Oneattempted solution to this issue is the use of delayed timer-basedcapture techniques. However, in delayed timer-based capture techniques,a user is often required to place the camera in a certain location andthen quickly join the group pose before the timer expires, which is achallenging action to take for many people or in many scenarios.Furthermore, photographs captured on a timer can have suboptimallighting effects and/or other undesirable image properties due, at leastin part, to the viewfinder of the camera not being used in an effectivemanner (the user having been required to leave the camera to join theshot) at the time of image capture. Furthermore, photographs captured ona timer often fail to have all persons in the group looking at thecamera, as certain persons may lose focus while the timer runs or may beunaware that the timer is set to expire. Group self-portraits, which area mixture of the two photograph types described above, often suffer fromthe same or similar problems.

SUMMARY

Aspects and advantages of the present disclosure will be set forth inpart in the following description, or may be obvious from thedescription, or may be learned through practice of embodiments of thepresent disclosure.

One example aspect of the present disclosure is directed to a computingsystem. The computing system includes an image capture system configuredto capture a plurality of image frames. The computing system includes anartificial intelligence system comprising one or more machine-learnedmodels. The artificial intelligence system is configured to analyze eachof the plurality of image frames and to output, for each of theplurality of image frames, a respective measure of one or moreattributes of a respective scene depicted by the image frame. Thecomputing system includes a display. The computing system includes oneor more processors and one or more non-transitory computer-readablemedia that store instructions that, when executed by the one or moreprocessors, cause the one or more processors to perform operations. Theoperations include providing, in a viewfinder portion of a userinterface presented on the display, a live video stream that depicts atleast a portion of a current field of view of the image capture system.The live video stream includes the plurality of image frames. Theoperations include providing, in the viewfinder portion of the userinterface presented on the display, a graphical intelligence feedbackindicator in association with the live video stream. The graphicalintelligence feedback indicator graphically indicates, for each of theplurality of image frames as such image frame is presented within theviewfinder portion of the user interface, the respective measure of theone or more attributes of the respective scene depicted by the imageframe output by the artificial intelligence system.

Another example aspect of the present disclosure is directed to acomputer-implemented method. The method includes obtaining, by one ormore computing devices, a real-time image stream comprising a pluralityof image frames. The method includes analyzing, by the one or morecomputing devices using one or more machine-learned models, each of theplurality of image frames to determine a respective image qualityindicator that describes whether content depicted in the respectiveimage frame satisfies a photographic goal. The method includesproviding, by the one or more computing devices, a feedback indicatorfor display in association with the real-time image stream in a userinterface, wherein the feedback indicator indicates the respective imagequality indicator for each image frame while such image frame ispresented in the user interface.

Another example aspect of the present disclosure is directed to acomputing system. The computing system includes an image capture systemconfigured to capture a plurality of image frames. The computing systemincludes an artificial intelligence system comprising one or moremachine-learned models. The artificial intelligence system is configuredto analyze each of the plurality of image frames and to output, for eachof the plurality of image frames, a respective measure of one or moreattributes of a respective scene depicted by the image frame. Thecomputing system includes a display. The computing system includes oneor more processors and one or more non-transitory computer-readablemedia that store instructions that, when executed by the one or moreprocessors, cause the one or more processors to perform operations. Theoperations include providing, in a viewfinder portion of a userinterface presented on the display, a live video stream that depicts atleast a portion of a current field of view of the image capture system.The live video stream includes the plurality of image frames. Theoperations include providing an intelligence feedback indicator inassociation with the live video stream, the intelligence feedbackindicator indicating, for each of the plurality of image frames as suchimage frame is presented within the viewfinder portion of the userinterface, the respective measure of the one or more attributes of therespective scene depicted by the image frame output by the artificialintelligence system.

Other aspects of the present disclosure are directed to various systems,apparatuses, non-transitory computer-readable media, user interfaces,and electronic devices.

These and other features, aspects, and advantages of the presentdisclosure will become better understood with reference to the followingdescription and appended claims. The accompanying drawings, which areincorporated in and constitute a part of this specification, illustrateembodiments of the present disclosure and, together with thedescription, serve to explain the principles of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art is set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1A depicts a block diagram of an example computing system accordingto example embodiments of the present disclosure.

FIG. 1B depicts a block diagram of an example computing device accordingto example embodiments of the present disclosure.

FIG. 1C depicts a block diagram of an example computing device accordingto example embodiments of the present disclosure.

FIG. 2 depicts a diagram of an example component arrangement accordingto example embodiments of the present disclosure.

FIG. 3 depicts a diagram of an example artificial intelligence systemaccording to example embodiments of the present disclosure.

FIG. 4 depicts a diagram of an example operational state flow accordingto example embodiments of the present disclosure.

FIGS. 5A-C depict an example user interface according to exampleembodiments of the present disclosure.

FIGS. 6-A-C depict a first example graphical intelligence feedbackindicator according to example embodiments of the present disclosure.

FIGS. 7-A-C depict a second example graphical intelligence feedbackindicator according to example embodiments of the present disclosure.

FIG. 8 depicts a third example graphical intelligence feedback indicatoraccording to example embodiments of the present disclosure.

FIG. 9 depicts a flow chart diagram of an example method according toexample embodiments of the present disclosure.

Reference numerals that are repeated across plural figures are intendedto identify the same features in various implementations.

DETAILED DESCRIPTION Overview

Generally, the present disclosure is directed to systems and methodsthat provide feedback to a user of an image capture device that includesan artificial intelligence system that analyzes incoming image framesto, for example, determine whether to automatically capture and storethe incoming frames. In particular, one example device or computingsystem (e.g., a smartphone) can include an image capture systemconfigured to capture a plurality of image frames and an artificialintelligence system configured to analyze each of the plurality of imageframes and output, for each of the plurality of image frames, arespective measure of one or more attributes of a respective scene, suchas lighting, depicted by the image frame. For example, the artificialintelligence system can output a score or other measure of how desirablea particular image frame is for satisfying a particular photographicgoal, such as, for example, a self-portrait photograph, a group portraitphotograph, and/or a group self-portrait photograph. In someimplementations, the artificial intelligence system can also beconfigured to automatically select certain images based for storage ontheir respective measures generated by the artificial intelligencesystem. The example system can provide, in a viewfinder portion of auser interface presented on a display, a live video stream that depictsat least a portion of a current field of view of the image capturesystem. In particular, the live video stream can include the pluralityof image frames. According to an aspect of the present disclosure, theexample system can also provide, in the viewfinder portion of the userinterface presented on the display, a graphical intelligence feedbackindicator in association with the live video stream. The graphicalintelligence feedback indicator can graphically indicate, for each ofthe plurality of image frames as such image frame is presented withinthe viewfinder portion of the user interface, the respective measure ofthe one or more attributes of the respective scene depicted by the imageframe output by the artificial intelligence system. Thus, in someimplementations, as the image frames are shown on the display inreal-time, the graphical intelligence feedback indicator can indicate orbe representative of the score or other measure of how desirable thecurrently shown image frame is for satisfying a particular photographicgoal, such as, for example, a self-portrait photograph, a group portraitphotograph, and/or a group self-portrait photograph. In particular, insome implementations, the feedback indicator can be viewed as a meterthat indicates a proximity of the artificial intelligence system toautomatic capture and non-temporary storage of imagery (e.g., how closethe image frame is to satisfying criteria for automatic capture andstorage). In such fashion, the user can be presented with real-timefeedback that informs the user of what, when, and why automatic capturedecisions are made by the artificial intelligence systems, which canenable users to participate in a collaborative image capture process.

Thus, through the use of an artificial intelligence system to select thebest shots and perform the shutter work, aspects of the presentdisclosure help users to capture and store images that best satisfytheir photographic goals (e.g., self-portraits, group portraits,landscape photography, traditional portraits, action scenes, or otherphotographic goals). In addition, the systems and methods of the presentdisclosure can provide real-time feedback that indicates a measuregenerated by the artificial intelligence system of one or moreattributes of the currently displayed view. As examples, the measuredattributes can include lighting in the image frame, the color of theimage frame, the presence and/or number of front facing faces, posingfaces, faces with smiling facial expressions, faces with unusual facialexpressions, faces with eyes open, and/or faces with frontal gaze. Inparticular, aspects of the present disclosure enable a continued andguided human-machine interaction, through which the user is providedfeedback, via the feedback indicator, of attributes of the image framesin real-time. This knowledge of the attributes of the image frames,which may for example include lighting and/or color properties of theimage frames, enables users to compose images with desired properties.As part of this, the user is able to step back and concentrate on posingfor or otherwise composing the image, letting the intelligence systemhandle the shutter control in an intelligent and hands-free way. Thisalso enables easier candid group shots by letting everyone get in theshot and capturing automatically and/or via remote triggers wheneveryone's looking their best.

Thus, a device can provide a feedback indicator that tells the user ifand/or to what degree the artificial intelligence system findsattributes of the current view appropriate, desirable, or otherwisewell-suited for a particular photographic goal such as a self or groupportrait. In such fashion, the systems and methods of the presentdisclosure can enable the collaboration between the user and theartificial intelligence system by guiding a user-machine interaction tocapture images that satisfy photographic goals. The user may for examplebe guided to change attributes of image frames being presented on thedevice, based on the real-time feedback, by moving to an area of theroom with different lighting conditions.

More particularly, an example device or computing system (e.g., asmartphone) can include an image capture system configured to capture aplurality of image frames. As one example, the image capture system caninclude a forward-facing camera that faces in a same direction as thedisplay. Although a smartphone with forward-facing camera is used as acommon example herein, aspects of the present disclosure are equallyapplicable to many other devices, systems, and camera configurations,including, for example, rearward-facing cameras.

The device or computing system can present a user interface on adisplay. The user interface can include a viewfinder portion. The deviceor system can present a live video stream that depicts at least aportion of a current field of view of the image capture system in theviewfinder portion of the user interface. More particularly, the devicecan display incoming image frames as they are received from the imagecapture system to provide the user with an understanding of the currentfield of view of the image capture system. Thus, as the user moves thedevice or otherwise changes the scene (e.g., by moving to a part of aroom with different lighting conditions or making a different facialexpression), the user can be given a real-time view of the image capturesystem's field of view.

The device or computing system can also include an artificialintelligence system configured to analyze each of the plurality of imageframes and output, for each of the plurality of image frames, arespective measure of one or more attributes of a respective scenedepicted by the image frame. For example, the artificial intelligencesystem can output a score or other measure of how desirable a particularimage frame is for satisfying a particular photographic goal, such as,for example, a self-portrait photograph, a group portrait photograph, ora group self-portrait photograph.

In some implementations, the artificial intelligence system can includeone or more machine-learned models such as, for example, amachine-learned face detection model, a machine-learned pose detectionmodel, and/or a machine-learned facial expression model. The artificialintelligence system can leverage the machine-learned models to determinethe measure of the attribute(s) of the image. For example, in someimplementations, the presence of one or more of the following in therespective scene results in an increase in the respective measure of theone or more attributes of the respective scene output by the artificialintelligence system: front facing faces; posing faces; faces withsmiling facial expressions; and/or faces with unusual facialexpressions.

In addition, according to an aspect of the present disclosure, thedevice or system can also provide, in the viewfinder portion of the userinterface presented on the display, a graphical intelligence feedbackindicator in association with the live video stream. The graphicalintelligence feedback indicator can graphically indicate, for each ofthe plurality of image frames as such image frame is presented withinthe viewfinder portion of the user interface, the respective measure ofthe one or more attributes of the respective scene depicted by the imageframe output by the artificial intelligence system. Thus, in someimplementations, as the image frames are shown on the display inreal-time, the graphical intelligence feedback indicator can indicate orbe representative of the score or other measure of how desirable thecurrently shown image frame is for satisfying a particular photographicgoal, such as, for example, a self-portrait photograph, a group portraitphotograph, or a group self-portrait photograph. This feedback cancontinuously guide the interaction between the user and the system so asto allow a shot satisfying the photographic goal. The final shot mayhave, for example, particular lighting and/or color properties and/orinclude certain subject matter (e.g., smiling faces facing toward thecamera).

Although portions of the present disclosure focus on graphicalindicators, aspects of the present disclosure are equally applicable toother types of feedback indicators including an audio feedback indicatorprovided by a speaker (e.g., changes in tone or frequency indicatefeedback), a haptic feedback indicator, an optical feedback indicatorprovided by a light emitter other than the display (e.g., changes inintensity or frequency of flash in light indicate feedback), and/orother types of indicators. Furthermore, although portions of the presentdisclosure focus on the photographic goals of self or group portraits,aspects of the present disclosure are equally applicable to other typesof photographic goals, including, for example, landscape photography,traditional portraits, action scenes, architectural photography, fashionphotography, or other photographic goals.

The graphical feedback indicators can take a number of different formsor styles and can operate in a number of different ways. As an example,in some implementations, the graphical intelligence feedback indicatorcan include a graphical bar that has a size that is positivelycorrelated to and indicative of the respective measure of the one ormore attributes of the respective scene depicted by the image framecurrently presented in the viewfinder portion of the user interface. Forexample, the graphical bar can be a horizontal bar at a bottom edge or atop edge of the viewfinder portion of the user interface.

In some implementations, the graphical bar can have a center point andextend along a first axis. In some implementations, the graphical barcan be fixed or pinned at the center point of the graphical bar and canincrease or decrease in size in both directions from the center point ofthe graphical bar along the first axis to indicate changes in therespective measure of the one or more attributes of the respective scenedepicted by the image frame currently presented in the viewfinderportion of the user interface.

Thus, in one example, if the user is watching the viewfinder and thegraphical bar, they may see the graphical bar grow or shrink as thescene becomes more or less desirable. For example, if the user turns hisface away from the camera the bar may shrink while if the user turns hisface towards the camera the bar may grow. Likewise, if the user frownsthen the bar may shrink while if the user smiles then the bar may grow.In some implementations, when the bar hits the edge of the display, thismay indicate that the device has decided to automatically capture aphotograph. As described further below, this may also be accompaniedwith an automatic capture notification. Thus, the user can be given thesense that, as the bar grows, so does the likelihood that an image willbe automatically captured.

In another example, in some implementations, the graphical intelligencefeedback indicator can include a graphical shape (e.g., circle,triangle, rectangle, arrow, star, sphere, box, etc.). An amount of thegraphical shape that is filled can be positively correlated to andindicative of the respective measure of the one or more attributes ofthe respective scene depicted by the image frame currently presented inthe viewfinder portion of the user interface.

In one particular example, the graphical shape (e.g., circle) can have acenter point. The amount of the graphical shape (e.g., circle) that isfilled can increase and decrease radially from the center point of theshape toward a perimeter of the shape to indicate changes in therespective measure of the one or more attributes of the respective scenedepicted by the image frame currently presented in the viewfinderportion of the user interface.

In some implementations, in addition or alternatively to the examplefeedback indicators described above, the graphical intelligence feedbackindicator can include textual feedback (e.g., displayed in theviewfinder portion of the user interface). For example, the textualfeedback can provide one or more suggestions to improve the measure ofthe one or more attributes of the respective scene. In some instances,the one or more suggestions can be generated by the artificialintelligence system or based on output of the artificial intelligencesystem.

In some implementations, the graphical intelligence feedback indicatorcan be viewed as or operate as a meter that indicates a proximity of theartificial intelligence system to automatic capture and non-temporarystorage of imagery. For example, the feedback indicator can fill and/orincrease in size to indicate how close the artificial intelligencesystem is approaching to automatically capturing and storing an image.

In some implementations, the graphical intelligence feedback indicatorgraphically indicates, for each image frame, a raw measure of the one ormore attributes of the respective scene depicted by the image framewithout reference to the measures of any other image frames. In otherimplementations, the graphical intelligence feedback indicatorgraphically indicates, for each image frame, a relative measure of theone or more attributes of the respective scene depicted by the imageframe relative to the previous respective measures of the one or moreattributes of respective image frames that have previously beenpresented within the viewfinder portion of the user interface. Forexample, the relative measure can be relative to images that have beencaptured during the current operational session of the device or system,during the current capture session, and/or since the last instance ofautomatic capture and storage. Thus, in some implementations,characteristics (e.g., size) of the graphical intelligence feedbackindicator can be determined based on measures of attribute(s) of thecurrent frame as well as a history of frames that have been seen and/orprocessed recently.

More particularly, as indicated above, in some implementations, thedevice or system can automatically store a non-temporary copy of atleast one of the plurality of image frames based at least in part on therespective measure output by the artificial intelligence system of theone or more attributes of the respective scene depicted by the at leastone of the plurality of image frames. For example, if the measure of theattribute(s) for a particular image frame satisfies one or morecriteria, the device or system can store a copy of the image in anon-temporary memory location (e.g., flash memory or the like). Incontrast, image frames that are not selected for storage can bediscarded without non-temporary storage. For example, image frames canbe placed in a temporary image buffer, analyzed by the artificialintelligence system, and then deleted from the temporary image buffer(e.g., on a first-in-first-out basis), such that only those images thatwere selected for non-temporary storage are retained following operationof the device and clearing of the buffer.

In some implementations, in response to automatically storing thenon-temporary copy of at least one of the plurality of image frames, thedevice or system can provide an automatic capture notification (e.g., inthe viewfinder portion of the user interface presented on the display).For example, the automatic capture notification can include a flashwithin the viewfinder portion of the user interface presented on thedisplay. The automatic capture notification can indicate to the userthat an image was captured (e.g., stored in a non-temporary memorylocation). This enables the user to understand the operation of theartificial intelligence system and to participate in the photoshootprocess.

In some implementations, after automatically storing the non-temporarycopy of at least one of the plurality of image frames, the device orsystem can operate in a refractory mode for a refractory period. In therefractory mode the computer system does not automatically storeadditional non-temporary copies of additional image frames regardless ofthe respective measure of the one or more attributes of the respectivescene depicted by the additional image frames. Alternatively oradditionally, in the refractory mode, the measure output but theartificial intelligence system and/or the graphical feedback indicatorcan be depressed to a lower level than such items would otherwise be ifthe device were not operating in the refractory mode. Operation in therefractory mode can avoid the situation where multiple, nearly identicalframes are redundantly captured and stored. Operation in the refractorymode can also provide a natural “pause” that is reflected in thecollaborative feedback from the device to the user, which can be anatural signal for the user to change poses and/or facial expressions,similar to behavior that occurs naturally when taking sequentialphotographs in a photoshoot.

In some implementations, the device or system can operate in a number ofdifferent operational modes and the auto-capture and/or feedbackoperations can be aspects of only a subset of such different operationalmodes. Thus, in some implementations, the device or system can receive auser input that requests operation of the computing system in aphotobooth mode and, in response to the user input, operate in thephotobooth mode, where providing, in the viewfinder portion of the userinterface presented on the display, the graphical intelligence feedbackindicator in association with the live video stream is performed as partof the photobooth mode. As an example, the device or system may betoggled between the photobooth mode and one or more other modes such asa traditional capture mode, a video mode, etc. Being a dedicated modepresents the user with an opportunity to choose to engage in temporaryauto-capture. Alternatively, the device or system can always provide theauto-capture and/or feedback operations regardless of the currentoperational mode of the device or system.

The systems and methods of the present disclosure are applicable to anumber of different use cases. As one example, the systems and methodsof the present disclosure enable (e.g., via a guided interaction processbetween a user and a device) easier capture of group photos. Inparticular, in one illustrative example, a user can set down hersmartphone, place the smartphone into an auto-capture mode, and let thesmartphone operate like a photographer who knows just what to look for.As another example, the systems and methods of the present disclosureenable easier (e.g., via the same or a similar guided interactionprocess) capture of solo self-portraits. In particular, in oneillustrative example, a user can hold up her smartphone to take aself-portrait to share on social media. Through the use of theauto-capture mode, the user can receive feedback regarding attributes ofcurrent image frames and focus on composing the image to be captured,for example by smiling and posing rather than operating the camerashutter. In effect, the user can turn her phone into a photobooth, havefun, and just pick out her favorites later. As yet another example, thesystems and methods of the present disclosure enable easier capture ofgroup self-portraits. In particular, in one illustrative example,instead of requiring a user to attempt to capture the image at exactlythe right time when everyone is looking at the camera with their eyesopen, the group can simply gather in front of the camera, receivefeedback on the attributes of current image frames and interact with theartificial intelligence system, for example by changing position and/orfacial expression based on the feedback, to cause the artificialintelligence system to capture images with particular attributes. In yetanother example use case, the user can hold the camera device (e.g.,smartphone) and point it at a subject (e.g., rearward-facing camerapointed at a subject other than the user). The user can let theintelligence handle the frame selection while the user is stillresponsible still for camera positioning, scene framing, and/or subjectcoaching.

In each of the example use cases described above, the smartphone canprovide feedback during capture that indicates to the user or group ofusers how they can improve the likelihood of automatic image capture andalso the quality of the captured images. In such fashion, the user(s)can be presented with real-time feedback that informs the user(s) ofwhat, when, and why automatic capture decisions are made by theartificial intelligence systems, which can enable users to participatein a collaborative image capture process. Through such collaborativeprocess, the automatically captured images can capture the candid,fleeting, genuine facial expressions that only artificial intelligenceis fast and observant enough to reliably capture.

The systems and methods of the present disclosure provide a number oftechnical effects and benefits. As one example, the systems and methodsdescribed herein can automatically capture images using minimalcomputational resources, which can result in faster and more efficientexecution relative to capturing and storing a large number of images innon-temporary memory and then reviewing the stored image frames toidentify those worth keeping. For example, in some implementations, thesystems and methods described herein can be quickly and efficientlyperformed on a user computing device such as, for example, a smartphonebecause of the reduced computational demands. As such, aspects of thepresent disclosure can improve accessibility of image capture using suchdevices, for example, in scenarios in which cloud computing isunavailable or otherwise undesirable (e.g., for reasons of improvinguser privacy and/or reducing communication cost).

In this way, the systems and methods described herein can provide a moreefficient operation of mobile image capture. By storing only the best,automatically selected images, the efficiency with which a particularimage can be extracted and stored in non-temporary memory can beimproved. In particular, the capture of brief and/or unpredictableevents such as a laugh or smile can be improved. The systems and methodsdescribed herein thus avoid image capture operations which are lessefficient, such as burst photography followed by manual culling.

In addition, through the use of feedback indicators, the user is able tomore efficiently collaborate with the artificial intelligence system. Inparticular, the user is given a sense of what will result in automaticimage capture and storage and can modify their behavior or other scenecharacteristics to more quickly achieve automatic capture and storage ofimages that suit the photographic goal. Thus, the use of feedback canresult in the device or system obtaining high-quality results in lessoperational time, thereby saving operational resources such asprocessing power, battery usage, memory usage, and the like.

In various implementations, the systems and methods of the presentdisclosure can be included or otherwise employed within the context ofan application, an application plug-in (e.g., browser plug-in), as afeature of an operating system, as a service via an applicationprogramming interface, or in other contexts. Thus, in someimplementations, the machine-learned models described herein can beincluded in or otherwise stored and implemented by a user computingdevice such as a laptop, tablet, or smartphone. As yet another example,the models can be included in or otherwise stored and implemented by aserver computing device that communicates with the user computing deviceaccording to a client-server relationship. For example, the models canbe implemented by the server computing device as a portion of a webservice (e.g., a web image capture service).

With reference now to the Figures, example embodiments of the presentdisclosure will be discussed in further detail.

Example Devices and Systems

FIG. 1A depicts a block diagram of an example computing system 100according to example embodiments of the present disclosure. The system100 includes a user computing device 102, a server computing system 130,and a training computing system 150 that are communicatively coupledover a network 180.

The user computing device 102 can be any type of computing device, suchas, for example, a personal computing device (e.g., laptop or desktop),a mobile computing device (e.g., smartphone or tablet), a gaming consoleor controller, a wearable computing device, an embedded computingdevice, or any other type of computing device.

The user computing device 102 includes one or more processors 112 and amemory 114. The one or more processors 112 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 114can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof The memory 114 can store data 115and instructions 116 which are executed by the processor 112 to causethe user computing device 102 to perform operations.

The memory 114 can include a non-temporary memory location 117 and atemporary image buffer 118. For example, the temporary image buffer 118can be a ring buffer. The temporary image buffer 118 can correspond witha non-transitory computer-readable storage medium that is suited fortemporary storage of information, such as RAM, for example. For example,the temporary image buffer 118 can include volatile memory. Thenon-temporary memory location 117 may correspond with a non-transitorycomputer-readable storage medium that is suited for non-temporarystorage of information, such as flash memory device, magnetics discs,etc. For example, the non-temporary memory location 117 can includenon-volatile memory.

In some implementations, the user computing device can include anartificial intelligence system 119. The artificial intelligence system119 can be configured to analyze each of a plurality of image frames andoutput, for each of the plurality of image frames, a respective measureof one or more attributes of a respective scene depicted by the imageframe. For example, the artificial intelligence system 119 can output ascore or other measure of how desirable a particular image frame is forsatisfying a particular photographic goal, such as, for example, aself-portrait photograph, a group portrait photograph, or a groupself-portrait photograph.

In some implementations, the artificial intelligence system 119 can beconfigured to capture content that features people and faces wheresubjects are in-focus and not blurry and/or subjects are smiling orexpressing positive emotions. The artificial intelligence system 119 canavoid capturing subjects who have their eyes closed or are blinking.

Thus, in some implementations, the artificial intelligence system 119can detect human faces in view and prioritize capture when faces arewithin 3-8 feet away and central to the camera's FOV (e.g., not withinthe outer 10% edge of view). In some implementations, the artificialintelligence system 119 can detect and prioritize capturing positivehuman emotions. For example, the artificial intelligence system 119 candetect smiling, laughter, and/or other expressions of joy, such assurprise, and contentment.

In some implementations, the artificial intelligence system 119 candetect human gaze and eyes to prioritize capture when subjects arelooking at the camera and/or avoid blinks or closed eyes in selectedmotion photo poster frames. In some implementations, the artificialintelligence system 119 can detect and prioritize capturing clips whenfaces are known to be in-focus and properly exposed according toauto-focus/auto-exposure attributes defined by camera application APIs.

In some implementations, the artificial intelligence system 119 canprioritize capture when the is a reasonable confidence that the camerais set down or held stably (e.g., use IMU data to avoid delivering“shakycam” shots). In some implementations, the artificial intelligencesystem 119 can activity detection. In some implementations, theartificial intelligence system 119 can perform automatic cropping.

In some implementations, the artificial intelligence system 119 canstore or include one or more machine-learned models 120. For example,the machine-learned models 120 can be or can otherwise include variousmachine-learned models such as neural networks (e.g., deep neuralnetworks) or other types of machine-learned models, including non-linearmodels and/or linear models. Neural networks can include feed-forwardneural networks, recurrent neural networks (e.g., long short-term memoryrecurrent neural networks), convolutional neural networks or other formsof neural networks. Example machine-learned models 120 are discussedwith reference to FIG. 3.

In some implementations, the one or more machine-learned models 120 canbe received from the server computing system 130 over network 180,stored in the user computing device memory 114, and then used orotherwise implemented by the one or more processors 112. In someimplementations, the user computing device 102 can implement multipleparallel instances of a single machine-learned model 120 (e.g., toperform analysis of multiple images in parallel).

Additionally or alternatively, one or more machine-learned models 140can be included in or otherwise stored and implemented by the servercomputing system 130 that communicates with the user computing device102 according to a client-server relationship. For example, themachine-learned models 140 can be implemented by the server computingsystem 140 as a portion of a web service. Thus, one or more models 120can be stored and implemented at the user computing device 102 and/orone or more models 140 can be stored and implemented at the servercomputing system 130.

The user computing device 102 can also include one or more user inputcomponent 122 that receives user input. For example, the user inputcomponent 122 can be a touch-sensitive component (e.g., atouch-sensitive display screen or a touch pad) that is sensitive to thetouch of a user input object (e.g., a finger or a stylus). Thetouch-sensitive component can serve to implement a virtual keyboard.Other example user input components include a microphone, a traditionalkeyboard, or other means by which a user can provide user input.

The user computing device 102 can include a display 124. The display 124can be any type of display including, for example, a cathode ray tubedisplay, a light-emitting diode display (LED), an electroluminescentdisplay (ELD), an electronic paper or e-ink display, a plasma displaypanel (PDP), a liquid crystal display (LCD), an organic light-emittingdiode display (OLED), and/or the like.

The user computing device 102 can include an image capture system 126that is configured to capture images. The image capture system 126 caninclude one or more cameras. Each camera can include various components,such as, for example, one or more lenses, an image sensor (e.g., a CMOSsensor or a CCD sensor) an imaging pipeline (e.g., image signalprocessor), and/or other components.

The camera(s) can be any type of camera positioned according to anyconfiguration. In one example, the device 102 can have multipleforward-facing cameras and/or multiple rearward-facing cameras. Thecameras can be narrow angle cameras, wide angle cameras, or acombination thereof. The cameras can have different filters and/or bereceptive to different wavelengths of light (e.g., one infrared cameraand one visible light spectrum camera). In one example, the device 102can have a first rearward-facing camera (e.g., with a wide-angle lensand/or f/1.8 aperture), a second rearward-facing camera (e.g., with atelephoto lens and/or f/2.4 aperture), and a frontward-facing camera(e.g., with a wide-angle lens and/or f/2.2 aperture). In anotherparticular example, the device 102 can include the following cameras: arearward-facing camera (e.g., with 12.2-megapixel, laser autofocus,and/or dual pixel phase detection), a first frontward-facing camera(e.g., with 8.1-megapixel and/or f/1.8 aperture), and a secondfrontward-facing camera (e.g., with 8.1-megapixel, wide-angle lens,and/or variable f/1.8 and f/2.2 aperture).

The server computing system 130 includes one or more processors 132 anda memory 134. The one or more processors 132 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 134can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof The memory 134 can store data 136and instructions 138 which are executed by the processor 132 to causethe server computing system 130 to perform operations.

In some implementations, the server computing system 130 includes or isotherwise implemented by one or more server computing devices. Ininstances in which the server computing system 130 includes pluralserver computing devices, such server computing devices can operateaccording to sequential computing architectures, parallel computingarchitectures, or some combination thereof.

As described above, the server computing system 130 can store orotherwise include one or more machine-learned models 140. For example,the models 140 can be or can otherwise include various machine-learnedmodels. Example machine-learned models include neural networks or othermulti-layer non-linear models. Example neural networks include feedforward neural networks, deep neural networks, recurrent neuralnetworks, and convolutional neural networks. Example models 140 arediscussed with reference to FIG. 3.

The user computing device 102 and/or the server computing system 130 cantrain the models 120 and/or 140 via interaction with the trainingcomputing system 150 that is communicatively coupled over the network180. The training computing system 150 can be separate from the servercomputing system 130 or can be a portion of the server computing system130.

The training computing system 150 includes one or more processors 152and a memory 154. The one or more processors 152 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 154can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof. The memory 154 can store data 156and instructions 158 which are executed by the processor 152 to causethe training computing system 150 to perform operations. In someimplementations, the training computing system 150 includes or isotherwise implemented by one or more server computing devices.

The training computing system 150 can include a model trainer 160 thattrains the machine-learned models 120 and/or 140 stored at the usercomputing device 102 and/or the server computing system 130 usingvarious training or learning techniques, such as, for example, backwardspropagation of errors. In some implementations, performing backwardspropagation of errors can include performing truncated backpropagationthrough time. The model trainer 160 can perform a number ofgeneralization techniques (e.g., weight decays, dropouts, etc.) toimprove the generalization capability of the models being trained.

In particular, the model trainer 160 can train the machine-learnedmodels 120 and/or 140 based on a set of training data 162. The trainingdata 162 can include, for example, processed images and/or unprocessedimages as training images.

Thus, in some implementations, the model trainer 160 can train newmodels or update versions on existing models on additional image data.The training data 162 can include images that have been labeled withground truth measures or one or more attributes of interest. As anexample, the model trainer 160 can use images hand-labeled as beingdesirable to train one or more models to provide outputs regarding thedesirability of an input image. In particular, in some implementations,the additional training data can be images that the user created orselected through an editing interface. Thus, updated versions of themodels can be trained by model trainer 160 on personalized data sets tobetter infer, capture, and store images which satisfy the particularvisual tastes of the user. In other instances, the additional trainingdata can be anonymized, aggregated user feedback.

Thus, in some implementations, if the user has provided consent, thetraining examples can be provided by the user computing device 102.Thus, in such implementations, the model 120 provided to the usercomputing device 102 can be trained by the training computing system 150on user-specific data received from the user computing device 102. Insome instances, this process can be referred to as personalizing themodel.

The model trainer 160 includes computer logic utilized to providedesired functionality. The model trainer 160 can be implemented inhardware, firmware, and/or software controlling a general purposeprocessor. For example, in some implementations, the model trainer 160includes program files stored on a storage device, loaded into a memoryand executed by one or more processors. In other implementations, themodel trainer 160 includes one or more sets of computer-executableinstructions that are stored in a tangible computer-readable storagemedium such as RAM hard disk or optical or magnetic media.

The network 180 can be any type of communications network, such as alocal area network (e.g., intranet), wide area network (e.g., Internet),or some combination thereof and can include any number of wired orwireless links. In general, communication over the network 180 can becarried via any type of wired and/or wireless connection, using a widevariety of communication protocols (e.g., TCP/IP, HTTP, SMTP, FTP),encodings or formats (e.g., HTML, XML), and/or protection schemes (e.g.,VPN, secure HTTP, SSL).

FIG. 1A illustrates one example computing system that can be used toimplement the present disclosure. Other computing systems can be used aswell. For example, in some implementations, the user computing device102 can include the model trainer 160 and the training dataset 162. Insuch implementations, the models 120 can be both trained and usedlocally at the user computing device 102. In some of suchimplementations, the user computing device 102 can implement the modeltrainer 160 to personalize the models 120 based on user-specific data.

FIG. 1B depicts a block diagram of an example computing device 10 thatperforms according to example embodiments of the present disclosure. Thecomputing device 10 can be a user computing device or a server computingdevice.

The computing device 10 includes a number of applications (e.g.,applications 1 through N). Each application contains its own machinelearning library and machine-learned model(s). For example, eachapplication can include a machine-learned model. Example applicationsinclude a text messaging application, an email application, a dictationapplication, a virtual keyboard application, a browser application, etc.

As illustrated in FIG. 1B, each application can communicate with anumber of other components of the computing device, such as, forexample, one or more sensors, a context manager, a device statecomponent, and/or additional components. In some implementations, eachapplication can communicate with each device component using an API(e.g., a public API). In some implementations, the API used by eachapplication is specific to that application.

FIG. 1C depicts a block diagram of an example computing device 50 thatperforms according to example embodiments of the present disclosure. Thecomputing device 50 can be a user computing device or a server computingdevice.

The computing device 50 includes a number of applications (e.g.,applications 1 through N). Each application is in communication with acentral intelligence layer. Example applications include a textmessaging application, an email application, a dictation application, avirtual keyboard application, a browser application, etc. In someimplementations, each application can communicate with the centralintelligence layer (and model(s) stored therein) using an API (e.g., acommon API across all applications).

The central intelligence layer includes a number of machine-learnedmodels. For example, as illustrated in FIG. 1C, a respectivemachine-learned model (e.g., a model) can be provided for eachapplication and managed by the central intelligence layer. In otherimplementations, two or more applications can share a singlemachine-learned model. For example, in some implementations, the centralintelligence layer can provide a single model (e.g., a single model) forall of the applications. In some implementations, the centralintelligence layer is included within or otherwise implemented by anoperating system of the computing device 50.

The central intelligence layer can communicate with a central devicedata layer. The central device data layer can be a centralizedrepository of data for the computing device 50. As illustrated in FIG.1C, the central device data layer can communicate with a number of othercomponents of the computing device, such as, for example, one or moresensors, a context manager, a device state component, and/or additionalcomponents. In some implementations, the central device data layer cancommunicate with each device component using an API (e.g., a privateAPI).

FIG. 2 depicts a schematic of an example image processing framework 200according to an example embodiment of the present disclosure. Inparticular, the schematic depicted in FIG. 2 illustrates relationshipsbetween components which permit multiple potential data paths or workflows through the framework 200. The image processing framework 200 canbe included in the user computing device 102 of FIG. 1A.

FIG. 2 provides one example of an image processing framework, but thepresent disclosure is not limited to the example provided in FIG. 2.Other configurations of image processing frameworks with more or fewercomponents and/or differing data flows can be used in accordance withthe present disclosure.

Referring to FIG. 2, the image processing framework 200 includes animage sensor 202 which outputs raw image data. For example, the rawimage data can be a Bayer RAW image. The raw image data can becommunicated to a first memory 204 and/or an imaging pipeline 206. Asone example, the first memory 204 which stores the raw image data outputby the image sensor 202 can be denominated as a raw temporary databuffer and can be, for example, DRAM memory. In some implementations,the imaging pipeline 206 streams the raw image data directly from theimage sensor 202. In such scenario, the temporary data buffer mayoptionally store processed images instead of the raw image data.

The imaging pipeline 206 takes the raw image data received from theimage sensor 202 and processes such raw image data to generate an image.For example, the processed image can be a RGB image, a YUV image, aYCbCr image, or images according to other color spaces. In addition, theimaging pipeline 206 can be operatively connected to a system processor214. The system processor 214 can include hardware blocks 216 thatassist the imaging pipeline 206 in performing Debayer filtering, RAWfiltering, LSC filtering, or other image processing operations. The RAWfilter stage can provide image statistics 216 for auto exposure in realtime and/or auto white balance operations. Software filters optionallymay be employed as well.

Depending on the capture mode of the mobile image capture device and/orother parameters, the imaging pipeline 206 can provide the image to anoptional scaler 208 or a second memory 222, which will be discussedfurther below. The scaler 208 can down sample the received image tooutput a lower resolution version of the image. Thus, in someimplementations, the scaler 208 can be denominated as a down sampler.

The scaler 208 provides the image to a third memory 210. The thirdmemory 210 may be the same memory as or a different memory than thesecond memory 222. The second memory 222 and/or the third memory 210 canstore temporary copies of the image. Thus, the second memory 222 and/orthe third memory 210 can be denominated as temporary image buffers. Insome implementations, the second memory 222 and/or the third memory 210are DRAM. In addition, in some implementations, downsampling can beperformed at the beginning of the imaging pipeline such that the imagingpipeline is enabled to run at a lower resolution and conserve power to agreater degree.

The second memory 222 and/or the third memory 210 can provide the imageinformation to an artificial intelligence system 212. In someimplementations, the artificial intelligence system 212 is operable toanalyze a scene depicted by the image to assess a desirability of suchscene and, based at least in part on such desirability, determinewhether to store a non-temporary copy of such image or to discard thetemporary copy of such image without further storage. The artificialintelligence system 212 can also access various data 218 stored at thesystem processor 214.

If the artificial intelligence system 212 determines that anon-temporary copy of the image should be stored, then the artificialintelligence system 212 can provide the image to a compression component226. In other implementations, the compression component 226 can receivethe image from the second memory 222 and/or the third memory 210. In yetother implementations, if the artificial intelligence system determinesthat a non-temporary copy of the image should be stored, then the rawimage data stored in the first memory 204 will be retrieved andprocessed by the imaging pipeline 206 and the resulting processed imagewill be provided to the compression component 226.

The compression component 226 compresses the received image. Thecompression component 226 can be a hardware component or imagecompression software implemented on a processor (e.g., the systemprocessor 214). After compression, a non-temporary copy of the image iswritten to a non-volatile memory 228. For example, the non-volatilememory 228 can be an SD card or other type of non-temporary memory.

It should be noted that, in some implementations, the image compressionpath 220 marked in a dotted box may not be active when an image is notchosen for compression and storage. Thus, in some implementations, theoutput of the artificial intelligence system 212 can be used to eitherturn on the image compression path 220 or control the image sensor 202.In particular, the artificial intelligence system 212 (e.g., inpartnership with the system processor 214) can provide sensor controlsignals 230 to control the image sensor 202, as will be discussedfurther below. Further, in some implementations, the output of theartificial intelligence system 212 can be used to either turn on or offthe imaging pipeline path as well. In addition, in some implementationsand/or capture modes, portions of the scene analysis can be performedwith respect to low-resolution images whereas other portions of thescene analysis can be performed on crops of high-resolution images(e.g., facial expression analysis may require crops of high resolutionimages).

In some implementations, the output from the image sensor 202 cancontrol most of the timing through the imaging pipeline 206. Forexample, image processing at the imaging pipeline 206 can be roughlyframe-synced to transfer at the image sensor receiver (e.g., an MIPIreceiver). Each of the stages of image processing 206 can have somedelay which causes the output to be a few image sensor rows behind theinput. This delay amount can be constant given the amount of processingthat happens in the pipeline 206.

In some implementations, the artificial intelligence system 212 canstart shortly after the imaging pipeline 206 has written all the linesof one image to memory. In other implementations, the artificialintelligence system 212 starts even before the imaging pipeline 206 haswritten all the lines of one image to memory. For example, certainmodels included in the artificial intelligence system (e.g., a facedetector model) can operate on subsets of the image at a time andtherefore do not require that all of the lines of the image are writtento memory. In some implementations, compression can be performed afterthe artificial intelligence system 212 determines that the image isworth saving and compressing. In other implementations, instead ofanalyzing images that have been fully processed by the image processingpipeline 206, the artificial intelligence system 212 can analyze Bayerraw images or images that have only been lightly processed by theimaging pipeline 206.

FIG. 3 depicts an example configuration 1200 of models in an artificialintelligence system according to an example embodiment of the presentdisclosure. FIG. 3 depicts different components operating in theartificial intelligence system and the data flow between them. Asillustrated, certain portions of the execution can be parallelized.

FIG. 3 provides one example of an artificial intelligence system, butthe present disclosure is not limited to the example provided in FIG. 3.Other configurations of an artificial intelligence system with more orfewer components and/or differing data flows can be used in accordancewith the present disclosure.

The following discussion with reference to FIG. 3 will refer to variousmodels. In some implementations, one or more (e.g., all) of such modelsare artificial neural networks (e.g., deep neural networks). Each modelcan output at least one descriptor that describes a measure of anattribute of the image. The image can be annotated with suchdescriptor(s). Thus, the outputs of the models will be referred to asannotations. In some implementations, the models provide the annotationsto a save controller 1250 which annotates the image with theannotations.

The configuration 1200 receives as input a frame of imagery 1202. Forexample, the frame 1202 may have been selected by a model scheduler foranalysis.

The frame of imagery 1202 is provided to a face detection or trackingmodel 1204. The face detection or tracking model 1204 detects one ormore faces depicted by the frame 1202 and outputs one or more facebounding boxes 1206 that describe the respective locations of the one ormore detected faces. The face bounding boxes 1206 can be annotated tothe frame 1202 and can also be provided as input alongside the frame1202 to a face attribute model 1208 and a face recognition model 1216.

In some implementations, the face detection or tracking model 1204performs face tracking rather than simple face detection. In someimplementations, the model 1204 may choose which of detection ortracking to perform. Face tracking is a faster alternative to facedetection. Face tracking can take as additional inputs the facedetection bounding boxes 1206 from a previous frame of imagery. The facetracking model 1204 updates the position of the bounding boxes 1206, butmay not in some instances detect new faces.

Importantly, neither face detection nor face tracking attempt todetermine or ascertain a human identity of any of the detected faces.Instead, the face detection or tracking model 1204 simply outputs facebounding boxes 1206 that describe the location of faces within the frameof imagery 1202. Thus, the model 1204 performs only raw detection of aface (e.g., recognition of depicted image features that are“face-like”), without any attempt to match the face with an identity.

The face attribute model 1208 can receive as input one or more crops ofthe frame of imagery 1202 (e.g., relatively higher resolution crops),where the one or more crops correspond to the portion(s) of the frame1202 defined by the coordinates of the bounding box(es) 1206. The faceattribute model 1208 can output an indication (e.g., a probability) thatthe detected face(s) include certain face attributes 1210. For example,the face attribute model 1208 can output respective probabilities thatthe detected faces include smiles, open eyes, certain poses, certainexpressions, a diversity of expression, or other face attributes 1210.

The face attributes 1210 can be provided as input alongside the frame ofimagery 1202 to a face photogenic model 1212. The face photogenic model1212 can output a single face score 1214 which represents a level ofphotogenicness of a pose, an expression, and/or other characteristics orattributes of the detected face(s).

Returning to the output of face detection or tracking model 1204, theface recognition model 1216 can receive as input one or more crops ofthe frame of imagery 1202 (e.g., relatively higher resolution crops),where the one or more crops correspond to the portion(s) of the frame1202 defined by the coordinates of the bounding box(es) 1206. The facerecognition model 1216 can output a face signature for each of thedetected faces. The face signature can be an abstraction of the facesuch as an embedding or template of the face or features of the face.

Importantly, the face recognition model 1216 does not attempt todetermine or ascertain a human identity of the detected face(s). Thus,the face recognition model 1216 does not attempt to determine a name forthe face or otherwise match the face to public profiles or other suchinformation. Instead, the face recognition model 1216 simply matches anabstraction of the detected face(s) (e.g., an embedding or otherlow-dimensional representation) to respective other abstractionsassociated with previously “recognized” faces. As one example, the facerecognition model 1216 may provide a probability (e.g., a level ofconfidence from 0.0 to 1.0) that an abstraction of a face depicted in aninput image matches an abstraction of a face depicted in a previouslycaptured image. Thus, the face recognition model 1216 may indicate(e.g., in the face signature 1218) that a face detected in the image1202 is likely also depicted in a previously captured image, but doesnot attempt to identify “who” this face belongs to in the human identitycontextual sense.

The frame of imagery 1202 can also be provided as input to an imagecontent model 1220. The image content model 1220 can output one or moresemantic feature vectors 1222 and one or more semantic labels 1224. Thesemantic feature vectors 1222 can be used for determining that twoimages contain similar content (e.g., similar to how face embeddings areused to determine that two faces are similar). The semantic labels 1224can identify one or more semantic features (e.g., “dog,” “sunset,”“mountains,” “Eiffel Tower,” etc.) detected within the frame of imagery1202. The notion of similarity between images can be used to ensure adiversity of captured images.

In some implementations, the image content model 1220 is a version of adeep convolutional neural network trained for image classification. Insome implementations, a subset of semantic classes that are particularlyimportant to users of the mobile image capture device (e.g., animals,dogs, cats, sunsets, birthday cakes, etc.) can be established and theimage content model 1220 can provide a particular emphasis ondetection/classification with respect to such subset of semantic classeshaving elevated importance.

The frame of imagery 1202 can also be provided as input to a visualfeature extractor model 1226. The visual feature extractor model 1226can output one or more visual feature vectors 1228 that describe one ormore visual features (e.g., a color histogram, color combinations, anindication of amount of blur, an indication of lighting quality, etc.)of the frame 1202.

The semantic feature vectors 1222, semantic labels 1224, and the visualfeature vectors 1228 can be provided as input alongside the frame 1202to a photo quality model 1230. The photo quality model 1230 can output aphoto quality score 1232 based on the inputs. In general, the photoquality model 1230 will determine the photo quality score 1232 on thebasis of an interestingness of the image 1202 (e.g., as indicated by thesemantic labels 1224), a technical quality of the image 1202 (e.g., asindicated by visual feature vectors 1228 that describe blur and/orlighting), and/or a composition quality of the image 1202 (e.g., asindicated by the relative locations of semantic entities and visualfeatures).

Some or all of the annotations 1206, 1210, 1214, 1218, 1222, 1224, 1228,and 1232 can be measures of attributes of the image frame 1202. In someimplementations, some or all of the annotations 1206, 1210, 1214, 1218,1222, 1224, 1228, and 1232 can be used to generate a single aggregatemeasure or score for the image frame 1202. In some implementations, thesingle score can be generated according to a heuristic such as, forexample, a weighted average of respective scores provided for theannotations, where the weightings of the weighted average and/or therespective scoring functions for respective annotations can be modifiedor tuned to score images against a particular photographic goal. In someimplementations, the single score can be used to control a feedbackindicator that is representative of the single score.

The save controller 1250 can take as input all of the annotations 1206,1210, 1214, 1218, 1222, 1224, 1228, and 1232 and make a decision whetheror not to save the frame of imagery 1202 or a high resolution versionthereof. In some implementations, the save controller 1250 will try tosave frames that the final curation function will want to select, andhence can be viewed as an online/real-time approximation to suchcuration function.

In some implementations, the save controller 1250 includes an in-memoryannotation index or other frame buffering so that save decisionsregarding frame 1202 can be made relative to peer images. In otherimplementations, the save controller 1250 makes decisions based only oninformation about the current frame 1202.

In some implementations, and to provide an example only, the savecontroller 1250 may be designed so that approximately 5% of capturedimages are selected for compression and storage. In someimplementations, whenever the save controller 1250 triggers storage ofan image, some window of imagery around the image which triggeredstorage will be stored.

In some implementations, various ones of the models can be combined toform a multi-headed model. As one example, the face attribute model1208, the face recognition model 1216, and/or the face photogenic model1212 can be merged or otherwise combined to form a multi-headed modelthat receives a single set of inputs and provides multiple outputs.

Configuration 1200 is provided as one example configuration only. Manyother configurations of models that are different than configuration1200 can be used by the artificial intelligence system. In particular,in some implementations, a model scheduler/selector of the artificialintelligence system can dynamically reconfigure the configuration ofmodels to which an image is provided as input.

FIG. 4 depicts a diagram of an example operational state flow accordingto example embodiments of the present disclosure. As illustrated in FIG.4, an example device can be toggled between operational states such as atraditional camera mode, a photobooth mode, and a photos gallery mode.

Thus, in some implementations, the photobooth operating mode can be adedicated mode accessed via a camera application mode switcher. Being adedicated mode presents the user with an opportunity to choose toparticipate in temporary auto-capture. In some implementations, exitingphotobooth mode and switching back to the main Camera mode can be easyand occur via a single button press.

In some implementations, the transition between the standard cameraapplication and photobooth mode can be seamless and can, for example, besignified by a screen fade to black and/or a brief display of aphotobooth icon announcing the mode switch. This transition time can beused to load intelligence models as needed.

In some implementations, when users are in the photobooth mode, theapplication can provide a real-time viewfinder to help the user frameshots and understand what's “in view” and/or give the user qualitativefeedback from camera intelligence to help them understand what the phone“sees.”

In some implementations, when users are in photobooth mode, it can bemade clear to users in as close to “present” as possible that new clipsare being captured so as to provide frequent feedback that the cameracapturing.

In some implementations, viewing recent shots can be one interaction(e.g., button press) away, and users can be able to easily delete shotsthat they don't want. Thumbnails of recent captures can represent thosefrom the current capture session and these thumbnails can be refreshedon each new instance of photobooth mode.

In some implementations, the first time a user launches photobooth mode,the user interface can provide a short tutorial on core concepts, suchas: the concept of hands-free capture; briefing on what intelligence“looks for”; recommended usage pattern, such as set down; current scopeof permissions; and/or other instructions.

In some implementations, while in photobooth mode, the image capturesystem can capture motion photos, selecting both an interestingup-to-3s, 30 fps video segment and a single high quality “poster frame.”In some implementations, the captured images can include full megapixeloutput from the front-facing camera sensor for the poster frame, HDR+,and/or 30 fps/720 p video component. The photobooth mode can utilize thestandard auto-exposure (AE) and auto-focus (AF) behavior from themainline camera application, as possible tuning for faces detected inview. In some implementations, various portrait mode effects (e.g.,bokeh blur) can be added to captured portrait photographs.

In some implementations, users can configure photo gallery backup & syncsettings for content captured in the photobooth mode separately fromcontent in a main directory. One way to do this might be to savephotobooth content in a specific device folder while still presentingsuch content in a main photos tab in the gallery. Users can search for,filter, and segment out clips captured in the photobooth mode in thephotos gallery.

In some implementations, users can be able to toggle audio captureon/off from the main screen of photobooth mode and/or from a settingsmenu. Alternatively or additionally, photobooth mode can inheritmainline camera options.

Example User Interfaces

FIGS. 5A-C depict an example user interface according to exampleembodiments of the present disclosure. The example user interfaceincludes a graphical intelligence feedback indicator 502 at a top edgeof a viewfinder portion 504 of the user interface.

As illustrated in FIGS. 5A-C, the graphical intelligence feedbackindicator 502 is a graphical bar that is horizontally oriented. In theillustrated example, the graphical intelligence feedback indicator 502indicates how suitable the presented image frame is for use as a groupportrait. In particular, the size of the graphical intelligence feedbackindicator 502 is positively correlated to an indicative of a measure ofsuitability for use as a group portrait that has been output by anartificial intelligence system.

More particularly, as shown in FIG. 5A, neither subject within thedepicted scene is looking at the camera. As such, the image isrelatively less desirable for satisfying a group portrait photographicgoal. Therefore, the size of the graphical intelligence feedbackindicator 502 is relatively small.

Turning to FIG. 5B, now one, but not both, of the subjects within thedepicted scene is looking at the camera. As such, the image isrelatively more desirable for satisfying a group portrait photographicgoal. Therefore, the size of the graphical intelligence feedbackindicator 502 has been increased relative to FIG. 5A.

Finally, turning to FIG. 5C, now both of the subjects within thedepicted scene are looking at the camera. As such, the image is highlysuitable for satisfying a group portrait photographic goal. Therefore,the size of the graphical intelligence feedback indicator 502 has beenincreased again relative to FIG. 5B. In fact, the size of the graphicalintelligence feedback indicator 502 in FIG. 5C now fills almost anentirety of a width the user interface. This may indicate that thedevice is about to or is currently automatically capturing an image.Stated differently, the line can grow to touch the edge of the displaywhen capture occurs.

The example user interface shown in FIGS. 5A-C can also optionallyinclude some or all of the following controls: a link to photo galleryviewer; a motion photos toggle control; zoom controls, user hints, amanual shutter button control, and/or a mode close control.

In some implementations, users can be provided with a simple way toincrease or decrease the capture rate of the camera when in thephotobooth mode, such as an optional slider setting or alternatively adiscrete number of levels. This can be an in-mode user interface that isseparate from the native settings of the camera. The slider or otherinterface feature may be accessed via a settings menu or may beavailable directly on the main interface screen.

FIGS. 6A-C depict a first example graphical intelligence feedbackindicator 602 according to example embodiments of the presentdisclosure. In particular, the graphical intelligence feedback indicator602 illustrated in FIGS. 6A-C is highly similar to that shown in FIGS.5A-C.

As illustrated in FIGS. 6A-C the graphical intelligence feedbackindicator 602 is a graphical bar that has a size that is positivelycorrelated to and indicative of the respective measure of the one ormore attributes of the respective scene depicted by the image framecurrently presented in the viewfinder portion of the user interface. Forexample, the graphical bar 602 is a horizontal bar at a top edge of theviewfinder portion of the user interface.

The graphical bar 602 has a center point 604 and extends along ahorizontal axis. The graphical bar 602 is fixed or pinned at the centerpoint 604 of the graphical bar 602 and increases or decreases in size inboth directions from the center point 604 of the graphical bar 602 alongthe horizontal axis to indicate changes in the respective measure of theone or more attributes of the respective scene depicted by the imageframe currently presented in the viewfinder portion of the userinterface. In some implementations, the entirety of the shape can befilled when capture is activated. Stated differently, the inner circlecan grow to touch the edge of the outer circle when capture occurs.

FIGS. 7-A-C depict a second example graphical intelligence feedbackindicator 702 according to example embodiments of the presentdisclosure. The graphical intelligence feedback indicator 702 is agraphical shape, which in the illustrated example is a circle. An amountof the graphical shape 702 that is filled is positively correlated toand indicative of the respective measure of the one or more attributesof the respective scene depicted by the image frame currently presentedin the viewfinder portion of the user interface.

The graphical shape (e.g., circle) 702 can have a center point 704. Theamount of the graphical shape (e.g., circle) 702 that is filledincreases and decreases radially from the center point 704 of the shape702 toward a perimeter of the shape 702 to indicate changes in therespective measure of the one or more attributes of the respective scenedepicted by the image frame currently presented in the viewfinderportion of the user interface.

FIG. 8 depicts a third example graphical intelligence feedback indicator802 according to example embodiments of the present disclosure. Theindicator 802 provides textual feedback. For example, the textualfeedback can provide one or more suggestions (e.g., “Face the Camera”)to improve the measure of the one or more attributes of the respectivescene. In some instances, the one or more suggestions can be generatedby the artificial intelligence system or based on output of theartificial intelligence system. Additional example suggestions include“hold the camera still”, “it's too dark”, “I don't see any faces”, “theflash is turned off” (or on), “there's not enough light”, “try differentlighting”, “try a different expression”, “move camera farther away”,“reduce backlighting”, and/or other suggestions. For example, thesuggestions can be descriptive of a primary reason why the artificialintelligence is not capturing an image or otherwise providing the imagewith a relatively lower score.

Example Methods

FIG. 9 depicts a flow chart diagram of an example method to performaccording to example embodiments of the present disclosure. AlthoughFIG. 9 depicts steps performed in a particular order for purposes ofillustration and discussion, the methods of the present disclosure arenot limited to the particularly illustrated order or arrangement. Thevarious portions of the method 900 can be omitted, rearranged, combined,and/or adapted in various ways without deviating from the scope of thepresent disclosure. For example, whether illustrated as such or not,various portions of the method 900 can be performed in parallel.

At 902, a computing system obtains an image frame from an image capturesystem.

At 904, the computing system stores the image frame in a temporary imagebuffer.

At 906, the computing system analyzes the image frame using anartificial intelligence system to determine a measure of one or moreattributes of a scene depicted by the image frame.

At 908, the computing system displays the image frame in a viewfinderportion of a user interface.

At 910, concurrently with 908, the computing system displays in theviewfinder portion of the user interface a graphical feedback indicatorthat indicates the measure of the one or more attributes of the imageframe currently displayed in the viewfinder portion of the userinterface.

At 912, the computing system determines whether the image framesatisfies one or more storage criteria. For example, the measure of theone or more attributes can be compared to the one or more criteria,which may, for example, take the form of threshold scores or conditionsthat must be met. In one particular example, images can satisfy storagecriteria if a certain percentage (e.g., >50%) of faces in the scene areexhibiting positive facial expressions. In another example, if more thana certain number (e.g., 3) of faces included in the scene are exhibitingpositive facial expressions, then the criteria can be consideredsatisfied.

If it is determined at 912 that the image frame does not satisfy thestorage criteria, then method 900 can return to 902 and obtain the nextimage frame in a stream of image frames from the image capture system.

However, if it is determined at 912 that the image frame does satisfythe storage criteria, then method 900 can proceed to 914.

At 914, the computing system can store the image frame in anon-temporary memory location.

At 916, the computing system can provide an automatic capturenotification in the user interface.

After 916, method 900 can return to 902 and obtain the next image framein a stream of image frames from the image capture system.

Additional Disclosure

The technology discussed herein makes reference to servers, databases,software applications, and other computer-based systems, as well asactions taken and information sent to and from such systems. Theinherent flexibility of computer-based systems allows for a greatvariety of possible configurations, combinations, and divisions of tasksand functionality between and among components. For instance, processesdiscussed herein can be implemented using a single device or componentor multiple devices or components working in combination. Databases andapplications can be implemented on a single system or distributed acrossmultiple systems. Distributed components can operate sequentially or inparallel.

While the present subject matter has been described in detail withrespect to various specific example embodiments thereof, each example isprovided by way of explanation, not limitation of the disclosure. Thoseskilled in the art, upon attaining an understanding of the foregoing,can readily produce alterations to, variations of, and equivalents tosuch embodiments. Accordingly, the subject disclosure does not precludeinclusion of such modifications, variations and/or additions to thepresent subject matter as would be readily apparent to one of ordinaryskill in the art. For instance, features illustrated or described aspart of one embodiment can be used with another embodiment to yield astill further embodiment. Thus, it is intended that the presentdisclosure cover such alterations, variations, and equivalents.

1. A computing system, comprising: an image capture system configured tocapture a plurality of image frames; an artificial intelligence systemcomprising one or more machine-learned models, the artificialintelligence system configured to analyze each of the plurality of imageframes and to output, for each of the plurality of image frames, arespective measure of one or more attributes of a respective scenedepicted by the image frame; a display; one or more processors; and oneor more non-transitory computer-readable media that store instructionsthat, when executed by the one or more processors, cause the one or moreprocessors to perform operations, the operations comprising: providing,in a viewfinder portion of a user interface presented on the display, alive video stream that depicts at least a portion of a current field ofview of the image capture system, wherein the live video streamcomprises the plurality of image frames; and providing, in theviewfinder portion of the user interface presented on the display, agraphical intelligence feedback indicator in association with the livevideo stream, the graphical intelligence feedback indicator graphicallyindicating, for each of the plurality of image frames as such imageframe is presented within the viewfinder portion of the user interface,the respective measure of the one or more attributes of the respectivescene depicted by the image frame output by the artificial intelligencesystem wherein the graphical intelligence feedback indicator comprises agraphical bar that has a size that is positively correlated to andindicative of the respective measure of the one or more attributes ofthe respective scene depicted by the image frame currently presented inthe viewfinder portion of the user interface.
 2. (canceled)
 3. Thecomputing system of claim 1, wherein the graphical bar comprises ahorizontal bar at a bottom edge or a top edge of the viewfinder portionof the user interface.
 4. The computing system of claim 1, wherein thegraphical bar has a center point and extends along a first axis, andwherein the graphical bar is fixed at the center point of the graphicalbar and increases or decreases in size in both directions from thecenter point of the graphical bar along the first axis to indicatechanges in the respective measure of the one or more attributes of therespective scene depicted by the image frame currently presented in theviewfinder portion of the user interface.
 5. The computing system ofclaim 1, wherein the graphical intelligence feedback indicator comprisesa graphical shape and wherein an amount of the graphical shape that isfilled with the graphical bar is positively correlated to and indicativeof the respective measure of the one or more attributes of therespective scene depicted by the image frame currently presented in theviewfinder portion of the user interface.
 6. (canceled)
 7. (canceled) 8.(canceled)
 9. (canceled)
 10. The computing system of claim 1, whereinthe graphical intelligence feedback indicator comprises a meter thatindicates a proximity of the artificial intelligence system to automaticcapture and non-temporary storage of imagery.
 11. (canceled) 12.(canceled)
 13. (canceled)
 14. (canceled)
 15. (canceled)
 16. Thecomputing system of claim 1, wherein the respective measure of the oneor more attributes of the respective scene depicted by each image framecomprises: a respective measure of one or more attributes of use of therespective scene as a self-portrait photograph; or a respective measureof one or more attributes of use of the respective scene as a groupphotograph.
 17. The computing system of claim 1, wherein the respectivemeasure of the one or more attributes of the respective scene depictedby each image frame comprises a respective measure of one or moreattributes of use of the respective scene as a group photograph.
 18. Thecomputing system of claim 1, wherein the computing system consists of amobile computing device that includes the image capture system, theartificial intelligence system, the display, the one or more processors,and the one or more non-transitory computer-readable media.
 19. Thecomputing system of claim 18, wherein the mobile computing devicecomprises a smartphone and the image capture system comprises aforward-facing camera that faces in a same direction as the display. 20.The computing system of claim 1, wherein the one or more machine-learnedmodels comprise one or more of: a machine-learned face detection model;a machine-learned pose detection model; or a machine-learned facialexpression model.
 21. The computing system of claim 1, wherein thepresence of one or more of the following in the respective scene resultsin an increase in the respective measure of the one or more attributesof the respective scene output by the artificial intelligence system:front facing faces; posing faces; faces with smiling facial expressions;faces with eyes open; faces with frontal gaze; or faces with unusualfacial expressions.
 22. The computing system of claim 1, wherein theoperations further comprise: receiving a user input that requestsoperation of the computing system in a photobooth mode; and in responseto the user input, operating the computing system in the photoboothmode, wherein said providing, in the viewfinder portion of the userinterface presented on the display, the graphical intelligence feedbackindicator in association with the live video stream is performed as partof the photobooth mode.
 23. The computing system of claim 1, wherein thegraphical intelligence feedback indicator graphically indicates, foreach of the plurality of image frames as such image frame is presentedwithin the viewfinder portion of the user interface, a relative measureof the one or more attributes of the respective scene depicted by theimage frame relative to the previous respective measures of the one ormore attributes of respective image frames that have previously beenpresented within the viewfinder portion of the user interface.
 24. Thecomputing system of claim 1, wherein the operations are performed inreal-time as the image capture system captures the plurality of imageframes.
 25. The computing system of claim 1, wherein the one or moreattributes of the respective scene depicted by each image framecomprises a desirability of the respective scene depicted by each imageframe or whether content depicted in the respective scene satisfies aphotographic goal.
 26. The computing system of claim 1, wherein the oneor more attributes of the respective scene depicted by each image framecomprises whether content depicted in the respective scene satisfies aphotographic goal.
 27. A computer-implemented method, the methodcomprising: obtaining, by one or more computing devices, a real-timeimage stream comprising a plurality of image frames; analyzing, by theone or more computing devices using one or more machine-learned models,each of the plurality of image frames to determine a respective imagequality indicator that describes whether content depicted in therespective image frame satisfies a photographic goal; and providing, bythe one or more computing devices, a feedback indicator for display inassociation with the real-time image stream in a user interface, whereinthe feedback indicator indicates the respective image quality indicatorfor each image frame while such image frame is presented in the userinterface; wherein the feedback indicator comprises a graphical bar thathas a size that is positively correlated to and indicative of the imagequality indicator of the respective scene depicted by the image framecurrently presented in the viewfinder portion of the user interface. 28.The computer-implemented method of claim 27, further comprising:selecting, by the one or more computing devices, at least one of theplurality of image frames for non-temporary storage based at least inpart its respective image quality indicator.
 29. Thecomputer-implemented method of claim 27, wherein the photographic goalcomprises a self-portrait or a group portrait.
 30. A computing system,comprising: an image capture system configured to capture a plurality ofimage frames; an artificial intelligence system comprising one or moremachine-learned models, the artificial intelligence system configured toanalyze each of the plurality of image frames and to output, for each ofthe plurality of image frames, a respective measure of one or moreattributes of a respective scene depicted by the image frame; a display;one or more processors; and one or more non-transitory computer-readablemedia that store instructions that, when executed by the one or moreprocessors, cause the one or more processors to perform operations, theoperations comprising: providing, in a viewfinder portion of a userinterface presented on the display, a live video stream that depicts atleast a portion of a current field of view of the image capture system,wherein the live video stream comprises the plurality of image frames;and providing an intelligence feedback indicator in association with thelive video stream, the intelligence feedback indicator indicating, foreach of the plurality of image frames as such image frame is presentedwithin the viewfinder portion of the user interface, the respectivemeasure of the one or more attributes of the respective scene depictedby the image frame output by the artificial intelligence system; whereinthe graphical intelligence feedback indicator comprises a graphical barthat has a size that is positively correlated to and indicative of therespective measure of the one or more attributes of the respective scenedepicted by the image frame currently presented in the viewfinderportion of the user interface. 31-35. (canceled)